{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "\n",
    "# scatter(x, y)\n",
    "A scatter plot of y vs. x with varying marker size and/or color.\n",
    "\n",
    "See `~matplotlib.axes.Axes.scatter`.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 200x200 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "\n",
    "plt.style.use('_mpl-gallery')\n",
    "\n",
    "# make the data\n",
    "np.random.seed(3)\n",
    "x = 4 + np.random.normal(0, 2, 24)\n",
    "y = 4 + np.random.normal(0, 2, len(x))\n",
    "# size and color:\n",
    "sizes = np.random.uniform(15, 80, len(x))\n",
    "colors = np.random.uniform(15, 80, len(x))\n",
    "\n",
    "# plot\n",
    "fig, ax = plt.subplots()\n",
    "\n",
    "ax.scatter(x, y, s=sizes, c=colors, vmin=0, vmax=100)\n",
    "\n",
    "ax.set(xlim=(0, 8), xticks=np.arange(1, 8),\n",
    "       ylim=(0, 8), yticks=np.arange(1, 8))\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.11.4"
  },
  "toc": {
   "base_numbering": 1,
   "nav_menu": {},
   "number_sections": false,
   "sideBar": true,
   "skip_h1_title": false,
   "title_cell": "Table of Contents",
   "title_sidebar": "Contents",
   "toc_cell": false,
   "toc_position": {},
   "toc_section_display": true,
   "toc_window_display": false
  }
 },
 "nbformat": 4,
 "nbformat_minor": 1
}
